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Abstract—Decision support systems (DSS) are one of the

most widely used management information systems in current

business management. The focus of the paper is on the

knowledge-based decision support systems, namely the KB-DSS,

in support of contemporary business management decision

making. Business managers use KB-DSS can improve their

decision making not only in terms of speed and accuracy but

also consistency. Key perspectives of KB-DSS including

technological, organizational, social and cultural perspectives

are discussed in the context of contemporary management

decision context. New contribution to the knowledge

management function of KB-DSS through a number of recent

projects is presented. The paper then highlights some

implications for the development of the next generation of

KB-DSS before a new architecture is proposed for future work.

Index Terms—Knowledge-based decision systems,

knowledge levels, knowledge reuse, knowledge mobilization,

critical knowledge, knowledge chain management, knowledge

life cycle.

I. INTRODUCTION

Business management decisions have been known for a

number of characteristics, for example, (1) their high

significance such as a lot of money are involved and usually a

lot of people’s interests are at stake; (2) time limitation –

many business decisions have to be made within very tight

time constraint in order for businesses to capture the market

opportunity; (3) high degree of complexity – business

decisions are often situated in rather complex environment

which needs to consider simultaneously the product and

service issues, supply chain issues, organizational issues,

quality issues, cost-effectiveness etc. (4) high degree of

uncertainty – business environment can change quickly,

especially under the current unstable financial situation

around the world, business decisions need to take account of

this uncertainty in order to adapt to the evolving global

market. Business managers have been facing great challenges

in making the right decisions when dealing with decision

situations that are extremely important, with high complexity

and uncertainty, and under time pressure. As a result,

business decision makers have been seeking for help from

technologies such as IT over the past few decades in order to

cope with the decision environment and make better

decisions. Various types of decision support systems (DSS)

have been developed and played an important role in

Manuscript received November 10, 2013; revised January 7, 2014.

The authors are with the Graduate School of Management, University of

Plymouth, UK (e-mail: [email protected]).

supporting business managers to improve business decisions

[1].

The classic DSS architecture is typically comprised of

three core components: a database management sub-system,

a model base sub-system, and a user interaction management

sub-system [2]. The DSS based on this classic architecture

have solved the decision support issue for business managers

to a certain extent: the DSS can provide the right information

at the right time in the right format; the DSS can provide the

right models with “what-if” analysis of decision alternatives;

the DSS can provide effective interaction mechanisms such

as decision dashboards so that information and analysis

results can be presented to decision makers in an

easy-to-understand manner. However, what these DSS lack

is that the systems cannot provide domain knowledge and

expertise to decision makers.

It has been acknowledged that the right decisions can only

be reached based on the decision makers’ good judgments,

and good judgments are based on good knowledge [3]. As the

scenarios a decision maker has to face are often complex, it is

hardly possible for decision makers to have all the knowledge

required to make those decisions without any help from

outside sources such as agents (software systems) and shared

work colleagues. Therefore, it is essential that decision

makers seek-out knowledge through appropriate knowledge

repository, reuse strategy and mechanisms. Furthermore,

decision makers could benefit tremendously from easy to use

knowledge systems that could equip them with extra

expertise and knowledge about the decision problems and

stimulate creative solutions to those problems [4]. To address

the above issues, the concept of a new generation of DSS

emerged in 1980s to include a knowledge management

function and the systems were named as knowledge-based

decision support systems (KB-DSS) [1], [5]-[7].

This paper will discuss some of the recent advancements in

KB-DSS from different perspectives, especially in terms of

the improvement of knowledge management function

through multiple knowledge levels, knowledge reuse,

knowledge mobilization, critical knowledge, knowledge

chain management and knowledge integration. The next

section will review related work in KB-DSS. Section III will

present outputs from a number of research projects

undertaken by the authors in terms of knowledge

management to improve KB-DSS. Implications for the

development of new KB-DSS for contemporary business

decisions are put forward together with a new architecture for

future work in Section IV. Finally, conclusions are drawn in

Section V.

Shaofeng Liu, Melanie Hudson Smith, Sarah Tuck, Jiang Pan, Ali Alkuraiji, and Uchitha Jayawickrama

498

Journal of Economics, Business and Management, Vol. 3, No. 5, May 2015

DOI: 10.7763/JOEBM.2015.V3.235

Where can Knowledge-Based Decision Support Systems

Go in Contemporary Business Management — A New

Architecture for the Future

II. RELATED WORK

The integration of the knowledge management function

into classic DSS can improve decision making performance

in two senses: (1) enhancing the quality of services by having

an “expert” readily available to users when human experts

are in short supply [4]; (2) assisting a human expert by

making their decisions more consistently [8]. Since 1990s,

knowledge management has been playing an important role

in the new generation of DSS known as KB-DSS. In today’s

rapidly changing business world, agile and flexible

organisations require their employees to frequently change

their work focus. Therefore KB-DSS with domain

knowledge can provide better support for decisions in

general, and specifically through facilitating integration of

decision models and decision processes (represented by

expert advice, generating alternatives and choice of choices).

Lying in the centre of knowledge management sub-system in

KB-DSS are a knowledge base and an inference engine [1].

Fig. 1 shows the core components in a KB-DSS that have

been widely accepted by scholars [5]-[7]. That is, a KB-DSS

has an additional component including a knowledge base and

an inference engine together with the three basic components

from the classic DSS: a database management sub-system

(DBMS), a model base management sub-system (MBMS)

and a user interaction management sub-system which is often

called a human-computer interface (HCI).

MBMSDBMS

Knowledge

base/Inference

engineHCIUser/

Decision maker

Fig. 1. Core components of KB-DSS.

KB-DSS has been mainly benefited from the advancement

in Artificial Intelligence (AI) technology in the past few

decades. AI technology has enabled the KB-DSS to have new

functions such as better knowledge modeling and reasoning

[9]. Many fully functioned knowledge-based systems (KBS)

have been developed and used in business decision making as

an independent system. However, some scholars have

suggested that KB-DSS should have a KBS to be integrated

into all three core components of a classic DSS, i.e. to include

a knowledge management function in the traditional DBMS,

MBMS and HCI of a DSS. For example, a knowledge

management function can be integrated into the DBMS to

help automatically update information and get rid of obsolete

data. An MBMS with knowledge management has the ability

to refine the decision models accordingly when the decision

situation changes. Integration of a knowledge management

function and an HCI will be able to have an intelligent

decision interface.

Besides the knowledge base, the other important artifact in

the knowledge management sub-system is the inference

engine which has reasoning mechanisms. An inference

engine is a piece of software programme that can refer to the

knowledge in the knowledge base, manipulate the knowledge

according to decision needs, and infer solutions and suggest

actions to be taken [9]. Most inference engines can not only

refer to knowledge in the knowledge base, but also can infer

new knowledge when needed. They also decide which, when

and the sequence existing knowledge can be activated.

Recent work in KB-DSS in terms of its reasoning function

can be classified around four important approaches:

rule-based reasoning (RBR), case-based reasoning (CBR),

network-based reasoning (NetBR) such as Bayesian

networks and artificial neural networks, and narrative-based

reasoning (NBR). RBR research has evolved significantly

from the traditional “if-then” rule to the modern belief rule

base which is capable of capturing vagueness,

incompleteness and nonlinear causal relationships in many

decision environments [10]. CBR systems do not need to

construct decision rules, but are valuable examples of

decision-support systems as they base their recommendations

on the subset of the most similar or most reusable experiences

previously encountered [11]. One of the main assets of CBR

is its eagerness to learn. Learning in CBR can be as simple as

memorizing a new case or can entail refining the memory

organization or meta-learning schemes. There has been

increasing use of NetBR in recent years. NetBR are

probabilistic inference engines that can be used to reason

under uncertainty. There is plenty of ongoing research on

integrating NetBR into a wide range of decision making

fields especially to solve complex semi-structured problems,

for example [12]. While KB-DSS based on RBR, CBR and

NetBR are mainly dealing with well-structured or

semi-structured knowledge, in the knowledge-intensive

industries such as social services, many business decisions

require support from knowledge that are hidden in

unstructured narratives such as clients’ records and stories,

from which NBR has emerged recently. Incorporating

narratives can help people have a more comprehensible

understanding of business decision challenges through

listening to others’ such as clients’ similar stories. KB-DSS

based on narrative-reasoning can equip decision makers to

better adapt to existing experience and discover more

innovative ideas to solve new decision problems [13].

Most exiting work on KB-DSS has focused on the

technology perspective such as IT technology, in particular

the AI and computer linguistics, to support knowledge

representation, reasoning and process to develop knowledge

management systems. Less effort has been committed to

understand the knowledge management requirements from

business decision makers, for example, business knowledge

acquisition and structuring, knowledge re-use, knowledge

mobilization, critical knowledge, knowledge chain

management, and knowledge lifecycle from organizational,

social and cultural perspectives. Current KB-DSS has been

criticized for a number of limitations, for example, the static

nature of the knowledge base; difficulty of re-using

knowledge; knowledge is understood from single point of

view, such as many scholars simply referred knowledge as

“know-how”; users lose confidence in knowledge because of

errors, obsolescent and unnecessary knowledge that make the

knowledge base crowded, messy and fat; lack of knowledge

refinement resulting in unsure about knowledge performance;

human-computer interface not intelligent enough to allow

499


two-way conversations between decision makers and

KB-DSS. Such gaps in the literature have motivated a

number of projects undertaken by the authors in recent years

with a focus on KB-DSS in contemporary business

management decision context, trying to overcome the

limitations. The next section discusses the contribution to

new knowledge from these research projects.

III. KEY PERSPECTIVES OF KB-DSS FROM RECENT

RESEARCH PROJECTS

This section presents the results of six research projects

recently undertaken by the authors to address the knowledge

support issue for DSS from different perspectives. The

projects made significant contribution to new knowledge in

KB-DSS, in particular in terms of knowledge acquisition and

structuring, knowledge reuse, knowledge mobilization,

critical knowledge identification, knowledge chain

management, and knowledge integration.

A. Project 1: Knowledge Levels and Structuring

How to better capture domain expert’s knowledge and

structure it in the knowledge base has been a long time

ongoing research topic. It seems to have two crucial

questions. One is how many levels should knowledge be

defined. The other question is how many knowledge

components or segments should be retained in a knowledge

base. Structuring knowledge base in how many segments or

components often depends on the nature of the domain

knowledge. These two questions have been investigated

through a project which was focused on developing a

KB-DSS for lean production management. In order to

consider both knowledge level and knowledge component

dimensions, the project created a knowledge acquisition and

structuring model which defines four levels of knowledge

and seven knowledge components [14]. A diagrammatic

illustration of the knowledge matrix is shown in Fig. 2.

Literature has widely discussed the “know-what” (i.e.

declarative knowledge) and “know-how” (i.e. procedural

knowledge) levels of knowledge in various knowledge

management scenarios. The contribution of the project is not

only to have investigated the “know-what” and “know-how”

in lean production management decision making, but also

implemented two new levels of knowledge , i.e. “know-why”

and “know-with” in the KB-DSS. One advantage of

implementing “know-why” in the KB-DSS is that the

systems can provide principles underlying “know-how”

and “know-what” for decision justification. The

“know-with” specifies the interrelationships among

knowledge components for integrated decision support

[14].

Overproduction

Excessiveprocesses

Waitingtime

Defectives

Excessive inventory

Excessive motion

Excessivetransport

Knowledge

components Knowledge layers

know-what• facts about problems and solutions

• declarative knowledge

know-how• how to reach solutions

• procedural knowledge

know-why• principles underlying know-what and know-

how

• decision justification/back up

know-with• inter-relationships between knowledge

components

• integrated decision support

Providing a structure for organising waste

elimination knowledge

Providing contentfor waste elimination

knowledge development

Fig. 2. Four levels of knowledge [14].

B. Project 2: Knowledge Reuse

Knowledge re-use in of course one of the main reasons

why KBS are developed in the first place. In terms of

decision support for business management, decision makers

can always make use of the existing knowledge in the

knowledge base to get better understanding of the market,

customers, resources, processes, and quantity control. If the

decision makers are novices in the business domain, through

re-using external knowledge of their peers, it is more likely

for them to have the chance to make the right business

decisions about products and services to create, supply chain

resources to recruit, marketing channels to explore, and

investment choices to gain maximum profit for the

companies. For expert decision makers, through knowledge

re-use, they can reapply proven solutions, learn from use and

failures, avoid pitfalls and increase the chances to make the

right decisions over time. There has been wide interest in

knowledge re-use research. One of the widely cited piece of

work published by Markus defined four types of users who

can benefit from knowledge-reuse, including shared work

producers (they create the knowledge themselves and later

reuse the knowledge), shared work practitioners (they do not

create knowledge but reuse knowledge created by their

co-workers), knowledge miners, and expertise seeking

novices [15]. A project undertaken by the authors developed

a structural knowledge re-use model which contains four

more key components in addition to Markus’s re-user types

and specifies the relationships between all five key

components [3]. The four extra components are Knowledge

Types (such as best practice, lessons learnt, rational

knowledge, procedural knowledge), Knowledge Sources (e.g.

repositories, systems, individual records), Knowledge

Environment (including model environment, system

environment, document environment, and knowledge base

environment), and Knowledge Integration Approaches (i.e.

network-based, traceability-based, and ontology-based). The

UML representation of the structural knowledge re-use

model is shown in Fig. 3.

Fig. 3. Knowledge re-use model in UML representation.

C. Project 3: Knowledge Mobilization

Investigating knowledge mobilization for decision support

is a necessity when organizations are going through changes

or when knowledge needs to be shared, transferred,

translated (because of culture difference) or disseminated in

communities or across supply chain partners. There are many

factors that could affect knowledge mobilization, including

organizational culture, organizational strategy,

organizational capacity and knowledge infrastructure. In

500


order to enable knowledge mobilization, it is important to

establish knowledge networks. A project currently being

undertaken by the authors has created a knowledge

mobilization model to support decision making in IT project

change management [16]. The knowledge mobilization

model defines connections between four types of knowledge

networks, including the knowledge networks of interaction,

knowledge networks of interpretation and translation,

knowledge networks of influence, and the institutional

knowledge networks (i.e. the knowledge base). The

knowledge mobilization model is centred around the classic

SECI (socialization, externalization, combination and

internalization) model proposed by Nonaka & Takeuchi [17].

By doing so, there is a potential that the right knowledge can

be transferred in the right format to the right people to

support them make the right decisions. Fig. 4 illustrates the

knowledge mobilization model.

Nonaka &Takeuchi

SECI Model 1995

Socialisation

Tacit to Tacit

Internalisation

Explicit to Tacit

Combination

Explicit to Explicit

Externalisation

Tacit to Explicit

Knowledge

Networks of

interactions

Knowledge

Transfer Knowledge

networks of

interpretation and

translations

Right

Format

(systems,

database)

Institutional

Knowledge

networks

(knowledge base)

Knowledge

Networks of

influence

Right

People

Powerful

Knowledge

and

information

Fig. 4. A knowledge mobilization model.

D. Project 4: Critical Knowledge

How much knowledge should be stored in the knowledge

base of a KB-DSS has remained as a key question under

investigation. It is easy to understand that keeping

unnecessary knowledge in the knowledge base will not only

cause waste of resource to hold and update the knowledge

base, but also cause distraction and confusion when users try

to query and retrieve the required knowledge from the

knowledge base. Identifying critical knowledge is a relatively

new topic and there has not been a concerted definition of

what critical knowledge is. Many scholars have defined

critical knowledge in various ways, for example, as “the

necessary knowledge to solve problems dealing with a given

objective and that should be capitalized” [18], “essential that

contributes to added value and business performance” [19],

“vital expertise, ideas and insights” [20], and “with regard to

its scarcity, cost and delay of acquisition” [21]. The authors’

current project develops a lean knowledge model that only

contains critical knowledge to optimise the knowledge

inventory for supply chain management decisions. The main

ideas of the lean knowledge model are shown in Fig. 5. It is

proposed that critical knowledge can be identified through

systematic processes such as using GAMETH method [22].

The identified critical knowledge can however still flow in

and out of the knowledge base all the time. Two strategies

can be used to optimize the knowledge inventory level, i.e.

just in time (JIT) and just in case (JIC) strategies, in order to

sustain a lean knowledge base [23].

Optimising Knowledge Inventory for

Supply Chain Management Decisions

Proposition•Support Global Supply Chain

Integration Decision

•Increase competitiveness

Identifying

Critical

Knowledge

Critical

Knowledge

Knowledge

Inventory

Strategies: JIT

vs. JIC

K outflowK Inflow

Lean

Knowledge

Model

Fig. 5. Critical knowledge in lean knowledge model.

E. Project 5: Knowledge Chain Management

When developing KB-DSS for supply chain management,

knowledge should not be seen as a static entity in the

knowledge repository, but as a dynamic process because

knowledge can change and evolve when it flows along

supply chains. Just as products and materials flow along

supply chains, knowledge chain has to be created and

maintained so that knowledge flow through supply chain

partners can be as smooth as possible. The earliest knowledge

chain model was only proposed at the beginning of 21

century which includes five primary knowledge activities

and four secondary knowledge activities, as shown in Fig. 6

[24]. Authors’ research extended Holsapple and Singh’s

knowledge chain model to enable decision support in global

supply chain management context by adding three new

knowledge chains to include global customer knowledge,

global capacity knowledge, and global supply network

configuration knowledge [25].

Glo

ba

l

Co

mp

etitiven

ess

Global market knowledge

Global capability knowledge

Global supply network

configuration knowledge

Acquisition Selection Generation

Internalisation Externalisation

Leadership

Co-ordination

Control

Measurement

Five primary

knowledge

activities

Four secondary

knowledge

activities

Three new

knowledge

chains

Fig. 6. Knowledge chain model to support decision making in global

supply chain management.

F. Project 6: Knowledge Integration

Knowledge integration has been recognized as a key issue

in all knowledge based systems, and KB-DSS is no exception.

A lot of scholars have put efforts in knowledge integration,

but so far progress still remains in conceptual models and

lacks mature software tools that can be readily integrated in

KB-DSS. The authors have explored an integrative

framework for knowledge management in support of ERP

(Enterprise Resource Planning) systems implementation

501


decisions. The framework recommends that knowledge

management should look at the whole knowledge life cycle

together with the types of knowledge and multiple levels of

knowledge, as illustrated in Fig. 7 [26]. The knowledge

lifecycle includes four stages: knowledge creation,

knowledge retention, knowledge transfer and knowledge

application. Specific knowledge types investigated in the

project are ERP package knowledge, organizational culture

knowledge, business process knowledge and project

management knowledge. The four levels of knowledge

specified in project 1 are explored here in a different decision

context, i.e. ERP implementation, they are know-what,

know-how, know-why and know-with. More importantly,

this project focused on the exploration of the links across the

knowledge types, knowledge layers and knowledge lifecycle.

Knowledge

creation

Knowledge

retention

Knowledge

transfer

Knowledge

application

KM life cycle

K-layers

K-types

Organisational

culture

knowledge

Business

process

knowledge

Project

management

knowledge

ERP

package

knowledge

Know-with

Know-why

Know-how

Know-what

Knowledge management

for ERP implementation

decisions

Fig. 7. An integrative knowledge framework for ERP implementation

decisions.

IV. IMPLICATIONS AND PROPOSED NEW ARCHITECTURE FOR

FUTURE WORK

Based on the outputs from the six projects undertaken by

the authors, we have learnt that knowledge management in

KB-DSS is multi-faceted and requires researchers’ greater

endeavor to gain insightful understanding of how KB-DSS

can be best used to improve decision maker’s decision

performance in real business world. However, some

implications can be highlighted for the development of more

intelligent KB-DSS:

1) Knowledge should not be seen as a static entity in the

knowledge base, but process-oriented as knowledge

evolves with the decision environment in organization,

society and culture.

2) Knowledge needs to be shared and re-used to

accommodate users’ different roles in different decision

situations.

3) Decision support needs multi-level of knowledge, i.e.

besides know-what and know-how, KB-DSS should

provide know-why for decision justification and

know-with for integrated decision support.

4) KB-DSS needs to consider knowledge validation and

evaluation to check the knowledge credibility and

criticality, to make sure the knowledge held in the

knowledge base is correct, current and that the

knowledge base is lean.

5) Knowledge held in KB-DSS needs to be refined

according to knowledge relationships, knowledge

performance and evolving business performance

objectives.

6) KB-DSS needs intelligent HCI so that decision makers

can use the systems to take better control of the decision

situations, define and specify the decision problems,

direct the decision processes, inquire and question the

systems more easily, and make improved judgment,

while the KB-DSS can better adapt to changing decision

situations, evolve with decision environment, explain

better about the rationale behind recommendations,

guide the decision makers to the right choices, learn

from the decision makers, suggest solutions and answer

decision maker’s queries. That means that KB-DSS can

act more than just an assistant but also a mentor and an

advisor which can stimulate decision maker’s innovative

ideas and critical thinking.

To knit the ideas together for the development of a

potentially improved KB-DSS, a new architecture is

proposed for future work, as illustrated in Fig. 8. It can be

seen from the Fig. 8, the knowledge management sub-system

in KB-DSS will have five new components in addition to the

existing knowledge base and inference engine (with

reasoning mechanisms). A meta-knowledge component will

allow the KB-DSS to implement multi-levels of knowledge

to include know-why and know-with on top of know-what

and know-how. A knowledge validation/ evaluation

component will allow the check for knowledge correctness,

precision, credibility and criticality. The knowledge

refinement component can imitate human experts so as to

analyze their knowledge and its effectiveness, learn from it,

and improve on it for future decision consultations. The

KB-DSS will be able to create user profiles based on their

knowledge to capture the user’s role in decision making

processes, their expertise level and their behavior in using the

systems over time. The knowledge traceability component

will facilitate knowledge mobilization and knowledge

integration when dealing with decision situations across

organizations and supply chains. In order to evaluate this new

architecture, future work would be implementing the system

and applying the system to various business decision cases.

KB

MBMSDBMS

Knowledge

base/Inference

engine

Meta-knowledge (multi-levels)

Knowledge validation/evaluation

Knowledge refinement

Inference/reasoning

User profilere knowledge

Knowledgetraceability

Fig. 8. A new architecture for future KB-DSS.

V. CONCLUSIONS

Knowledge-based decision support systems (KB-DSS)

have been investigated for nearly three decades and have

supported real business management decisions in many

industries. It has been recognized that KB-DSS have

improved decision maker’s performance, especially in terms

of speed and consistency. This paper presents new

502


contributions from six research projects undertaken by the

authors to further improve the knowledge management

function in KB-DSS with respect to knowledge levels and

structuring, knowledge re-use, knowledge mobilization,

critical knowledge, knowledge chain management and

knowledge integration. A new architecture is proposed for

future work in developing more intelligent KB-DSS.

ACKNOWLEDGMENT

The authors would like to thank a number of parties which

have funded the work presented in this paper, including UK

Engineering and Physical Science Research Council,

University of Plymouth and Saudi Arabia government.

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[23] J. Pang, S. Liu, S. Tuck, and A. Alkuraiji, “Optimising inventory level

of global critical knowledge for integrated decision support,” presented

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[26] U. Jayawickrama, S. Liu, and M. H. Smith, “An integrative knowledge

management framework to support ERP implementation for improved

management decision making in industry,” in Lecture Notes in

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86-101.

Shaofeng Liu is a professor of Operations

Management and Decision Making at the University of

Plymouth, UK. She obtained her Ph.D. degree from

Loughborough University, UK, specialising in

Knowledge and Information Management for Global

Manufacturing Co-ordination Decisions. Her main

research interests and expertise are in knowledge-based

techniques to support business decision making,

particularly in the areas of knowledge management,

integrated decision support, ERP systems and quantitative decision methods

for lean operations, process improvement, resource management, quality

management, and supply chain management. She is currently supervising 7

PhD students in above research areas. She has undertaken a number of

influential research projects funded by UK research councils and the

European Commission. She has published over 90 peer-reviewed research

papers including 50 journal articles, 5 book chapters, 23 conference papers,

and editorial for 6 journal Special Issues and 5 conference/workshop

proceedings. She is currently an associate editor for the Journal of Decision

Systems and on the Editorial Board for the International Journal of Decision

Support Systems Technology. She conducts regular review for 3 research

councils and 10 international journals.

Melanie Hudson Smith is a lecturer in Operations

Management at the University of Plymouth. Her

primary research interests are in operations

improvement, sustainable supply chains and service

quality, with recent publications in these areas. She

teaches Operations at both undergraduate and

postgraduate levels and supervises a number of

Ph.D. students.

Sarah Jane Tuck spent 12 years at sea as a deck

officer where she qualified as a master mariner. She

also served for a time in the royal naval reserve. She

undertook her Ph.D. at Plymouth University,

examining the socio-economics of small ports and

wharves in the southwest of England. She is presently

a lecturer in maritime business within the International

Shipping, Logistics & Operations group at Plymouth

University. Her research interests include sustainable

ports and shipping, qualitative research methods and short sea shipping. Her

research grants include a successful Knowledge Transfer Partnerships (KTP)

project with Falmouth Harbour Commissioners, and the Low Carbon

Shipping Consortium, both funded by the UK government.

503


Ali Alkhuraiji is a researcher at Graduate School of

Management, University of Plymouth, UK. He was

awarded an MSc degree by Loughborough

University, UK in 2011, specialising in Information

Science, in particular in information and knowledge

management. His area of research is on knowledge

management, IT projects, decision making and

knowledge mobilization. He has presented some of

his research at International conference held in Rome,

Italy at the 2013 Euro Conference

Uchitha Jayawickrama is currently a doctoral

researcher at Plymouth Business School, University of

Plymouth, United Kingdom. He worked as an Oracle

Applications Consultant for Oracle E-Business Suite

(ERP) systems implementation in various industries.

He is an MBA holder from University of Sri

Jayewardenepura, Sri Lanka. He obtained his BSc.

honours degree in Information Systems from Sri

Lanka Institute of Information Technology (SLIIT),

Sri Lanka. His main research and teaching interests are in ERP systems,

knowledge management, decision making and information systems. He has

published research papers in journals and presented his research studies at

different international conferences.

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